Today, substantial amounts of computer time are used essentially implementing Bayes formula to compute probabilities. For example, there exist on-line content distribution services that execute applications for predicting content that a consumer is likely to rate highly given content that the consumer has previously rated. Similarly, there exist retailing services that execute applications for predicting what products a consumer is likely to want to purchase given what that consumer has purchased before. Then, there exist search engines that attempt to predict what links might be relevant on the basis of search history. These applications essentially compute conditional probabilities, i.e. the probability of an event given the occurrence of prior events.
Other probabilistic applications include procedures for guessing how to translate a webpage from one language to another, and large-scale Bayesian inference, including synthetic aperture reconstruction in radar imaging, image reconstruction in medical tomography, and predicting nucleic acid sequences associated with diseases.
In the communications area, probabilistic computation arises when embedded and mobile applications in, for example, a cell phone, predict what bits were originally transmitted based on a received noisy signal. In robotics, there exist applications for predicting the most likely optimal path across difficult terrain.